{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MiniMax\n",
    "\n",
    "[MiniMax](https://api.minimax.chat/document/guides/embeddings?id=6464722084cdc277dfaa966a) offers an embeddings service.\n",
    "\n",
    "This example goes over how to use LangChain to interact with MiniMax Inference for text embedding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-24T15:13:15.397075Z",
     "start_time": "2023-05-24T15:13:15.387540Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"MINIMAX_GROUP_ID\"] = \"MINIMAX_GROUP_ID\"\n",
    "os.environ[\"MINIMAX_API_KEY\"] = \"MINIMAX_API_KEY\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-24T15:13:17.176956Z",
     "start_time": "2023-05-24T15:13:15.399076Z"
    }
   },
   "outputs": [],
   "source": [
    "from langchain_community.embeddings import MiniMaxEmbeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-24T15:13:17.193751Z",
     "start_time": "2023-05-24T15:13:17.182053Z"
    }
   },
   "outputs": [],
   "source": [
    "embeddings = MiniMaxEmbeddings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-24T15:13:17.844903Z",
     "start_time": "2023-05-24T15:13:17.198751Z"
    }
   },
   "outputs": [],
   "source": [
    "query_text = \"This is a test query.\"\n",
    "query_result = embeddings.embed_query(query_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-24T15:13:18.605339Z",
     "start_time": "2023-05-24T15:13:17.845906Z"
    }
   },
   "outputs": [],
   "source": [
    "document_text = \"This is a test document.\"\n",
    "document_result = embeddings.embed_documents([document_text])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-24T15:13:18.620432Z",
     "start_time": "2023-05-24T15:13:18.608335Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cosine similarity between document and query: 0.1573236279277012\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "query_numpy = np.array(query_result)\n",
    "document_numpy = np.array(document_result[0])\n",
    "similarity = np.dot(query_numpy, document_numpy) / (\n",
    "    np.linalg.norm(query_numpy) * np.linalg.norm(document_numpy)\n",
    ")\n",
    "print(f\"Cosine similarity between document and query: {similarity}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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